ESEA: Discovering the Dysregulated Pathways based on Edge Set Enrichment Analysis.

Junwei Han,Chunlong Zhang,Desi Shang,Li Feng,Ying Jiang,Xinrui Shi,Xia Li,Yanjun Xu,Fei Su,Yunpeng Zhang,Chunquan Li,Haixiu Yang,Zeguo Sun

doi:10.1038/srep13044

Abstract

Pathway analyses are playing an increasingly important role in understanding biological mechanism, cellular function and disease states. Current pathway-identification methods generally focus on only the changes of gene expression levels; however, the biological relationships among genes are also the fundamental components of pathways, and the dysregulated relationships may also alter the pathway activities. We propose a powerful computational method, Edge Set Enrichment Analysis (ESEA), for the identification of dysregulated pathways. This provides a novel way of pathway analysis by investigating the changes of biological relationships of pathways in the context of gene expression data. Simulation studies illustrate the power and performance of ESEA under various simulated conditions. Using real datasets from p53 mutation, Type 2 diabetes and lung cancer, we validate effectiveness of ESEA in identifying dysregulated pathways. We further compare our results with five other pathway enrichment analysis methods. With these analyses, we show that ESEA is able to help uncover dysregulated biological pathways underlying complex traits and human diseases via specific use of the dysregulated biological relationships. We develop a freely available R-based tool of ESEA. Currently, ESEA can support pathway analysis of the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther).

Highlights

The classical enrichment analysis methods are developed by using the statistical models, such as Fisher’s exact test and hypergeometric test, to detect if the differentially-expressed genes are over- or under-represented in a predefined pathway[6]
Pathway enrichment analysis (PWEA) calculates a score, called “Topological Influence Factor (TIF)”, for each gene by using the shortest distances between genes in pathways, and the degree of differential expression is weighted by their corresponding TIF to infer perturbed pathways[21]
We compared the results of Edge Set Enrichment Analysis (ESEA) with five other pathway enrichment analysis methods

Summary

Introduction

The classical enrichment analysis methods are developed by using the statistical models, such as Fisher’s exact test and hypergeometric test, to detect if the differentially-expressed genes are over- or under-represented in a predefined pathway[6]. Pathway enrichment analysis (PWEA) calculates a score, called “Topological Influence Factor (TIF)”, for each gene by using the shortest distances between genes in pathways, and the degree of differential expression is weighted by their corresponding TIF to infer perturbed pathways[21] These methods adopt the pathway structure information and achieve good results, they just use the pathway structure as evidences for connecting genes in pathways, whereas ignoring the changes of expression correlations between genes appearing in the pathway structure. We developed a powerful edge-centric method, Edge Set Enrichment Analysis (ESEA), to identify dysregulated pathways by investigating the changes of inherent biological relationships embedded in pathways in the context of gene expression data. We validated that ESEA can produce biologically meaningful outcomes

Methods

Results

Conclusion